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4. Marketing Actions 1 Product
4.2 Price
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Digital products and services have very low or zero marginal cost for production and distribution. This has important implications for pricing and revenue models, especially in the context of product lines consisting of traditional formats as well as digital formats. Venkatesh and Chatterjee (2006) examined optimal pricing of online and offline content (magazines and journals) and showed that the online format can lead to higher profits acting as a device for price discrimination. While they assumed consumers buy one or the other format, Kannan, Pope and Jain (2009) showed that consumers are heterogeneous in their perception of substitutability and complementarity of formats and that higher profits can be achieved by bundling formats.
Pauwels and Weiss (2007) focused on freemium models in the context of newspapers and magazines in the presence of advertising revenue. Kanuri et al. (2016) constructed a menu of content subscription bundles that maximizes total profit from both consumers and advertisers in the context of a similar newspaper platform and provided insights into profit maximizing menus under various business model and format strategies. Lambrecht and Misra (2016) focusing on content platforms examined the question of how much content should be free and when firms should charge a fee. They found that firms can increase revenue by flexibly adjusting the amount of content they offer against a fee instead of setting a static paywall as many content providers do. The flexibility depends on the heterogeneity in consumer demand and therefore can be dynamic.
Lee, Kumar and Gupta (2013) focused on ad-free freemium products like Dropbox and developed optimal pricing strategies using structural models. As firms create new types of digital goods and formats, academic research has followed and provided generalizable
understanding and recommendations. Other examples of recent research involve innovations in digital goods such as online music (Chung, Rust, and Wedel, 2009), video games (Liu, 2010),
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and cloud computing (Liu, Singh, and Srinivasan, 2015). Lambrecht et al. (2014) provided a review on content-based, information-based, and advertising-based revenue models for digital goods. In the content-based revenue model, the firm can sell content and services. The
information-based model applies when revenue is generated by a firm selling its customers' information, such as browsing behavior at the cookie level. The advertising-based revenue model is suited for websites that hold inventory for display ads. The space allocated for advertising can be an important driver of the firm's revenue.
The pricing for products and services online is more dynamic than in brick-and-mortar businesses for a number of reasons: (1) search costs for consumers are low, (2) menu costs for retailers are low, (3) changes in the shopping environment are rapid, and (4) retailers can respond to customers’ searches more quickly. Additionally, the increasing usage of auction in customer acquisition (e.g. search engines, re-targeting, etc.) brings in more selective customers to the retailers’ site. On the one hand, the customers are doing more price comparisons due to lower search costs. Degeratu, Rangaswamy, and Wu (2000) found that online shoppers are more price sensitive than offline shoppers. Using the data from air travel industry, Granados, Gupta, and Kauffman (2012) similarly showed that online demand is more elastic, partially due to the self- selection issue that more leisure travelers than business travelers reserve their air travel online.
On the other hand, online retailers are able to measure demand, track competitors’ prices and adjust prices faster due to lower menu costs. Kannan and Kopalle (2001) distinguished the information-based virtual value chain from the product-based physical value chain and discussed a few new pricing strategies emerging on the Internet, including the auction model, demand aggregation, dynamic posted prices, Priceline's reverse auction model, and others.
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A number of studies have focused on new models of pricing enabled by the digital environment and on the characteristics of pricing in the online market – name-your-own-price- channels (Hann and Terwiesch 2003; Spann and Tellis 2006; Fay 2004; Amaldoss and Jain 2008), online auctions (Popkowski Leszczyc and Häubl 2010; Haruvy and Popkowski Leszczyc 2010), and price dispersion in online markets (see Pan, Ratchford and Shankar 2004).
With IoT poised to take off, pricing of products augmented with digital services would be an important area of research. Prior research in online information pricing and access pricing is a useful starting point. Jain and Kannan (2002) examined the various ways online servers
providing access to databases charge consumers – connect-time pricing, flat-rate pricing for information downloaded, or subscription-based pricing for unlimited downloading. Essagaier, Gupta, and Zhang (2002) and Iyengar et al. (2011) have examined similar issues in the context of access-based pricing and a pricing structure for telecommunication services that would be useful for IoT applications. In a similar vein, Iyengar, Jedidi, and Kohli (2008) built a conjoint
analysis model to study consumer choices between contracts with three-part pricing (base fee, free usage allowance, and per-unit charge for usage exceeding the free allowance), as is common for telephone, mobile data, and car rental agreements. Their model simultaneously incorporated price and consumption levels into the conjoint analysis and captured the mutual dependence between price and consumption. They took into account consumer uncertainty about actual consumption levels, and found that ignoring such uncertainty would underestimate a consumer’s consumption level. Such pricing structures will become increasingly relevant in the future.
Pricing in the context of mobile and personal technologies is an area ripe for future research. These technologies along with voice and image based search may render search costs to be infinitesimally small. What are the implications of this for pricing and price matching?
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What are implications for price competition? Many firms are resorting to dynamic pricing on the Internet where the prices change depending on the time of day, the day of the week and other contextual situations. How will customers’ expectations be impacted by such pricing formats?
How can personal technologies enable firms to build customer loyalty and increase their pricing power?